Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
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TLDR
This work presents a way of thinking about machine learning that gives it its own place in the econometric toolbox, and aims to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble.Abstract:
Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the pre...read more
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Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.
TL;DR: The algorithms of machine learning, which can sift through vast numbers of variables looking for combinations that reliably predict outcomes, will improve prognosis, displace much of the work of radiologists and anatomical pathologists, and improve diagnostic accuracy.
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Artificial Intelligence (AI) : Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
Yogesh K. Dwivedi,Laurie Hughes,Elvira Ismagilova,Gert Aarts,Crispin Coombs,Tom Crick,Yanqing Duan,Rohita Dwivedi,John S. Edwards,Aled Eirug,Vassilis Galanos,P. Vigneswara Ilavarasan,Marijn Janssen,Paul Jones,Arpan Kumar Kar,Hatice Kizgin,Bianca Kronemann,Banita Lal,Biagio Lucini,Rony Medaglia,Kenneth Le Meunier-FitzHugh,Leslie Caroline Le Meunier-FitzHugh,Santosh K. Misra,Emmanuel Mogaji,Sujeet Kumar Sharma,Jang Bahadur Singh,Vishnupriya Raghavan,Ramakrishnan Raman,Nripendra P. Rana,Spyridon Samothrakis,Jak Spencer,Kuttimani Tamilmani,Annie Tubadji,Paul Walton,Michael D. Williams +34 more
TL;DR: This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
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The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning.
Sam Corbett-Davies,Sharad Goel +1 more
TL;DR: It is argued that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce, rather than requiring that algorithms satisfy popular mathematical formalizations of fairness.
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Financial time series forecasting with deep learning : A systematic literature review: 2005–2019
TL;DR: A comprehensive literature review on DL studies for financial time series forecasting implementations and grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM).
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Human Decisions and Machine Predictions
TL;DR: While machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
References
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Inference on Treatment Effects After Selection Amongst High-Dimensional Controls
TL;DR: This work develops a novel estimation and uniformly valid inference method for the treatment effect in this setting, called the "post-double-selection" method, which resolves the problem of uniform inference after model selection for a large, interesting class of models.
Journal ArticleDOI
Predicting poverty and wealth from mobile phone metadata
TL;DR: It is shown that an individual’s past history of mobile phone use can be used to infer his or her socioeconomic status and that the predicted attributes of millions of individuals can accurately reconstruct the distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households.
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Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain
TL;DR: A fully data-driven method for choosing the user-specified penalty that must be provided in obtaining LASSO and Post-LASSO estimates is provided and its asymptotic validity under non-Gaussian, heteroscedastic disturbances is established.
Journal ArticleDOI
Human Decisions and Machine Predictions
TL;DR: While machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
Journal ArticleDOI
Accelerating the discovery of materials for clean energy in the era of smart automation
Daniel P. Tabor,Loïc M. Roch,Semion K. Saikin,Christoph Kreisbeck,Dennis Sheberla,Joseph Montoya,Shyam Dwaraknath,Muratahan Aykol,Carlos Ortiz,Hermann Tribukait,Carlos Amador-Bedolla,Christoph J. Brabec,Benji Maruyama,Kristin A. Persson,Kristin A. Persson,Alán Aspuru-Guzik +15 more
TL;DR: It is envisioned that a closed-loop approach, which combines high-throughput computation, artificial intelligence and advanced robotics, will sizeably reduce the time to deployment and the costs associated with materials development.